Deep brain stimulation (DBS) is an effective surgical procedure for the treatment of a number of neurological and neuropsychiatric disorders, including medication-refractory Parkinson's disease (PD), essential tremor (ET), dystonia, and severe obsessive compulsive disorder. The procedure involves placing an electrode lead into a brain region to modulate abnormal neuronal activity with various forms of pulsatile electrical stimulation. Successful treatment can be characterized by both symptom suppression and lack of side-effects. Such success requires accurate lead placement as well as spatially targeted stimulation settings to avoid activating regions that elicit, for example, adverse motor, sensory, and/or cognitive side-effects for the patient.
Traditional designs of DBS leads (for example, the Medtronic Model 3387/3389) use four cylindrical electrodes to deliver current in an omnidirectional fashion around the lead. An improvement to this design is to enable the steering of current delivery both along and around the DBS lead, via, for example, circumferentially-segmented electrodes. Such a DBS array (DBSA) might have, for example, 32 electrodes arranged in eight rows of four electrodes each. These DBS arrays are especially useful in cases of off-target DBS implants and for small or complex-shaped brain targets, such as the subthalamic nucleus or the pedunculopontine nucleus.
With the larger number of electrodes available in a DBSA lead, programming, operation and/or control challenges have emerged. While manual testing of potential settings is feasible with traditional four electrode DBS leads, the number of possible combinations is unwieldy, and thus a need exists for efficient, effective, and safe methods of operating, controlling and/or programming DBSA settings, as conventional approaches to programming DBSAs have numerous drawbacks.
Conventional manual programming of DBSA settings works much like an optometrist performing a vision examination. A clinician will manually test many stimulation settings and evaluate the patient's response to each in order to determine the best one to use. This process can take hours within a single clinical visit and often several clinical visits over the course of weeks to months to optimize.
Conventional feedback-based systems have embedded technology to log and analyze patient response information or certain biomarkers (such as features in brain waves) in order to inform and update stimulation configurations. Sometimes a rating/ranking system is in place to determine the best configuration based on these responses/biomarkers.
Conventional brain mapping has been used by some researchers who have compiled intraoperative microelectrode stimulation data and mapped them onto a human brain atlas. An efficacy probability map is thus created by assigning every location on the brain atlas a probability for delivering therapeutic stimulation. The probability assignment is based on overlapping normal distributions with therapeutic stimulation sites at the center of each distribution. Finally, the efficacy probability map (brain atlas) is nonlinearly warped onto the patient's pre-operative MRI and used to determine the electrode settings that may deliver the best therapy. This approach is entirely based on empirical patient data.
Finally, conventional patient anatomy-based computational neuron models can be used to predict the best stimulating electrode settings for modulating a particular pathway or pathways within the brain. A therapeutic target volume in the brain is segmented and reconstructed from the patient's MRI data. The volume is populated with simulated model neurons and virtual stimulation is applied to them. The tissue enclosed by the activated neurons under virtual DBS is termed “volume of tissue activated” (VTA). Large numbers of simulations are run in order to account for the different stimulating electrode configurations, neuron orientations and locations. The solutions for each simulation are stored in a large lookup table. Given a new target volume for stimulation, the pre-compiled database can be searched for the setting that gives the most overlap between the solution VTA and the target volume.
In summary, conventional approaches for programming deep brain stimulation systems cannot scale well to DBSAs with more than a handful of electrodes. Manual and feedback-based programming methods can be tailored to the patient but can take too much time and resources to implement effectively. Mapping methods are limited by the availability of a sufficiently rich source database and do not take the unique structure of a patient's brain tissue into account. Finally, conventional computational models require vast computational resources that may not be present in a clinical setting.